

'''simple docstring'''
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class 					_snake_case		(__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase):
			__A  :	Any        =AltDiffusionPipeline
			__A  :	List[str]        =TEXT_TO_IMAGE_PARAMS
			__A  :	Optional[int]        =TEXT_TO_IMAGE_BATCH_PARAMS
			__A  :	List[Any]        =TEXT_TO_IMAGE_IMAGE_PARAMS
			__A  :	Dict        =TEXT_TO_IMAGE_IMAGE_PARAMS






			def 		UpperCamelCase__							(  self     ):
				torch.manual_seed(0     )
				UpperCAmelCase_     :      Tuple						=  UNetaDConditionModel(
				    block_out_channels=(32, 64)			,layers_per_block=2			,sample_size=32			,in_channels=4			,out_channels=4			,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")			,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")			,cross_attention_dim=32			,)
				UpperCAmelCase_     :      int						=  DDIMScheduler(
				    beta_start=0.00085			,beta_end=0.012			,beta_schedule="scaled_linear"			,clip_sample=_snake_case			,set_alpha_to_one=_snake_case			,)
				torch.manual_seed(0     )
				UpperCAmelCase_     :      Optional[int]						=  AutoencoderKL(
				    block_out_channels=[32, 64]			,in_channels=3			,out_channels=3			,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]			,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]			,latent_channels=4			,)

				# TODO: address the non-deterministic text encoder (fails for save-load tests)
				# torch.manual_seed(0)
				# text_encoder_config = RobertaSeriesConfig(
				#     hidden_size=32,
				#     project_dim=32,
				#     intermediate_size=37,
				#     layer_norm_eps=1e-05,
				#     num_attention_heads=4,
				#     num_hidden_layers=5,
				#     vocab_size=5002,
				# )
				# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

				torch.manual_seed(0     )
				UpperCAmelCase_     :      str						=  CLIPTextConfig(
				    bos_token_id=0			,eos_token_id=2			,hidden_size=32			,projection_dim=32			,intermediate_size=37			,layer_norm_eps=1E-05			,num_attention_heads=4			,num_hidden_layers=5			,pad_token_id=1			,vocab_size=50_02			,)
				UpperCAmelCase_     :      Union[str, Any]						=  CLIPTextModel(_snake_case     )

				UpperCAmelCase_     :      Optional[Any]						=  XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta"     )
				UpperCAmelCase_     :      List[Any]						=  77

				UpperCAmelCase_     :      Optional[Any]						=  {
				    "unet": unet,
				    "scheduler": scheduler,
				    "vae": vae,
				    "text_encoder": text_encoder,
				    "tokenizer": tokenizer,
				    "safety_checker": None,
				    "feature_extractor": None,
				}
				return components






			def 		UpperCamelCase__							(  self			,_snake_case			,_snake_case=0     ):
				if str(_snake_case     ).startswith("mps"     ):
					UpperCAmelCase_     :      int						=  torch.manual_seed(_snake_case     )
				else:
					UpperCAmelCase_     :      Dict						=  torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )
				UpperCAmelCase_     :      int						=  {
				    "prompt": "A painting of a squirrel eating a burger",
				    "generator": generator,
				    "num_inference_steps": 2,
				    "guidance_scale": 6.0,
				    "output_type": "numpy",
				}
				return inputs






			def 		UpperCamelCase__							(  self     ):
				super().test_attention_slicing_forward_pass(expected_max_diff=3E-3     )






			def 		UpperCamelCase__							(  self     ):
				super().test_inference_batch_single_identical(expected_max_diff=3E-3     )






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      str						=  "cpu"  # ensure determinism for the device-dependent torch.Generator

				UpperCAmelCase_     :      List[Any]						=  self.get_dummy_components()
				torch.manual_seed(0     )
				UpperCAmelCase_     :      Union[str, Any]						=  RobertaSeriesConfig(
				    hidden_size=32			,project_dim=32			,intermediate_size=37			,layer_norm_eps=1E-05			,num_attention_heads=4			,num_hidden_layers=5			,vocab_size=50_02			,)
				# TODO: remove after fixing the non-deterministic text encoder
				UpperCAmelCase_     :      Any						=  RobertaSeriesModelWithTransformation(_snake_case     )
				UpperCAmelCase_     :      int						=  text_encoder

				UpperCAmelCase_     :      int						=  AltDiffusionPipeline(**_snake_case     )
				UpperCAmelCase_     :      Dict						=  alt_pipe.to(_snake_case     )
				alt_pipe.set_progress_bar_config(disable=_snake_case     )

				UpperCAmelCase_     :      Any						=  self.get_dummy_inputs(_snake_case     )
				UpperCAmelCase_     :      Any						=  "A photo of an astronaut"
				UpperCAmelCase_     :      Tuple						=  alt_pipe(**_snake_case     )
				UpperCAmelCase_     :      Union[str, Any]						=  output.images
				UpperCAmelCase_     :      int						=  image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				UpperCAmelCase_     :      int						=  np.array(
				    [0.5748162, 0.60447145, 0.48821217, 0.50100636, 0.5431185, 0.45763683, 0.49657696, 0.48132733, 0.47573093]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      Optional[int]						=  "cpu"  # ensure determinism for the device-dependent torch.Generator

				UpperCAmelCase_     :      int						=  self.get_dummy_components()
				UpperCAmelCase_     :      Union[str, Any]						=  PNDMScheduler(skip_prk_steps=_snake_case     )
				torch.manual_seed(0     )
				UpperCAmelCase_     :      List[str]						=  RobertaSeriesConfig(
				    hidden_size=32			,project_dim=32			,intermediate_size=37			,layer_norm_eps=1E-05			,num_attention_heads=4			,num_hidden_layers=5			,vocab_size=50_02			,)
				# TODO: remove after fixing the non-deterministic text encoder
				UpperCAmelCase_     :      Union[str, Any]						=  RobertaSeriesModelWithTransformation(_snake_case     )
				UpperCAmelCase_     :      Tuple						=  text_encoder
				UpperCAmelCase_     :      List[str]						=  AltDiffusionPipeline(**_snake_case     )
				UpperCAmelCase_     :      int						=  alt_pipe.to(_snake_case     )
				alt_pipe.set_progress_bar_config(disable=_snake_case     )

				UpperCAmelCase_     :      Dict						=  self.get_dummy_inputs(_snake_case     )
				UpperCAmelCase_     :      List[Any]						=  alt_pipe(**_snake_case     )
				UpperCAmelCase_     :      Optional[Any]						=  output.images
				UpperCAmelCase_     :      int						=  image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				UpperCAmelCase_     :      Optional[int]						=  np.array(
				    [0.51605093, 0.5707241, 0.47365507, 0.50578886, 0.5633877, 0.4642503, 0.5182081, 0.48763484, 0.49084237]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2




@slow
@require_torch_gpu
class 					_snake_case		(unittest.TestCase):






			def 		UpperCamelCase__							(  self     ):
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()






			def 		UpperCamelCase__							(  self     ):
				# make sure here that pndm scheduler skips prk
				UpperCAmelCase_     :      Optional[Any]						=  AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"			,safety_checker=_snake_case     )
				UpperCAmelCase_     :      str						=  alt_pipe.to(_snake_case     )
				alt_pipe.set_progress_bar_config(disable=_snake_case     )

				UpperCAmelCase_     :      str						=  "A painting of a squirrel eating a burger"
				UpperCAmelCase_     :      Any						=  torch.manual_seed(0     )
				UpperCAmelCase_     :      Optional[Any]						=  alt_pipe([prompt]			,generator=_snake_case			,guidance_scale=6.0			,num_inference_steps=20			,output_type="np"     )

				UpperCAmelCase_     :      str						=  output.images

				UpperCAmelCase_     :      Optional[Any]						=  image[0, -3:, -3:, -1]

				assert image.shape == (1, 5_12, 5_12, 3)
				UpperCAmelCase_     :      Union[str, Any]						=  np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2






			def 		UpperCamelCase__							(  self     ):
				UpperCAmelCase_     :      List[str]						=  DDIMScheduler.from_pretrained("BAAI/AltDiffusion"			,subfolder="scheduler"     )

				UpperCAmelCase_     :      Union[str, Any]						=  AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion"			,scheduler=_snake_case			,safety_checker=_snake_case     )
				UpperCAmelCase_     :      int						=  alt_pipe.to(_snake_case     )
				alt_pipe.set_progress_bar_config(disable=_snake_case     )

				UpperCAmelCase_     :      List[Any]						=  "A painting of a squirrel eating a burger"
				UpperCAmelCase_     :      Optional[int]						=  torch.manual_seed(0     )

				UpperCAmelCase_     :      Dict						=  alt_pipe([prompt]			,generator=_snake_case			,num_inference_steps=2			,output_type="numpy"     )
				UpperCAmelCase_     :      int						=  output.images

				UpperCAmelCase_     :      List[Any]						=  image[0, -3:, -3:, -1]

				assert image.shape == (1, 5_12, 5_12, 3)
				UpperCAmelCase_     :      Union[str, Any]						=  np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323]     )

				assert np.abs(image_slice.flatten() - expected_slice     ).max() < 1E-2








"""simple docstring"""



import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
		raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
		raise Exception('requires fairseq < v2')

logging.set_verbosity_info()
lowercase_							=							logging.get_logger(__name__)

lowercase_							=							'Hello, World!'
lowercase_							=							'en_XX'
def      UpperCAmelCase     (       _lowercase :   str			,   _lowercase :   str			,   _lowercase :   bool	)						-> Optional[int]:


	"""simple docstring"""




	lowerCAmelCase_      							=			Path('''data_bin'''	)
	lowerCAmelCase_      							=			FairseqXmodModel.from_pretrained(
	    model_name_or_path=str(Path(_lowercase	).parent	)			,   checkpoint_file=Path(_lowercase	).name			,   _name='''xmod_base'''			,   arch='''xmod_base'''			,   task='''multilingual_masked_lm'''			,   data_name_or_path=str(_lowercase	)			,   bpe='''sentencepiece'''			,   sentencepiece_model=str(Path(_lowercase	).parent / '''sentencepiece.bpe.model'''	)			,   src_dict=str(data_dir / '''dict.txt'''	)			,   )
	xmod.eval()  # disable dropout
	print(_lowercase	)

	lowerCAmelCase_      							=			xmod.model.encoder.sentence_encoder
	lowerCAmelCase_      							=			XmodConfig(
	    vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings			,   hidden_size=xmod.cfg.model.encoder_embed_dim			,   num_hidden_layers=xmod.cfg.model.encoder_layers			,   num_attention_heads=xmod.cfg.model.encoder_attention_heads			,   intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim			,   max_position_embeddings=5_1_4			,   type_vocab_size=1			,   layer_norm_eps=1E-5			,   pre_norm=xmod.cfg.model.encoder_normalize_before			,   adapter_reduction_factor=getattr(xmod.cfg.model			,   '''bottleneck'''			,   2	)			,   adapter_layer_norm=xmod.cfg.model.adapter_layer_norm			,   adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm			,   ln_before_adapter=xmod.cfg.model.ln_before_adapter			,   languages=xmod.cfg.model.languages			,   )
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight.shape[0]

	print('''Our X-MOD config:'''			,   _lowercase	)

	lowerCAmelCase_      							=			XmodForSequenceClassification(_lowercase	) if classification_head else XmodForMaskedLM(_lowercase	)
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_tokens.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.embed_positions.weight
	lowerCAmelCase_      							=			torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight	)  # just zero them out b/c xmod doesn't use them.

	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.weight
	lowerCAmelCase_      							=			xmod_sent_encoder.layernorm_embedding.bias

	for i in range(config.num_hidden_layers	):
		# Encoder: start of layer
		lowerCAmelCase_      							=			model.roberta.encoder.layer[i]
		lowerCAmelCase_      							=			xmod_sent_encoder.layers[i]

		# self attention
		lowerCAmelCase_      							=			layer.attention.self
		if not (
		    xmod_layer.self_attn.k_proj.weight.data.shape
		    == xmod_layer.self_attn.q_proj.weight.data.shape
		    == xmod_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)	)
		):
			raise AssertionError('''Dimensions of self-attention weights do not match.'''	)

		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.q_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.k_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.v_proj.bias

		# self-attention output
		lowerCAmelCase_      							=			layer.attention.output
		if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
			raise AssertionError('''Dimensions of self-attention output weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn.out_proj.bias
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.self_attn_layer_norm.bias

		# intermediate
		lowerCAmelCase_      							=			layer.intermediate
		if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of intermediate weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias

		# output
		lowerCAmelCase_      							=			layer.output
		if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
			raise AssertionError('''Dimensions of feed-forward weights do not match.'''	)
		lowerCAmelCase_      							=			xmod_layer.fca.weight
		lowerCAmelCase_      							=			xmod_layer.fca.bias
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.weight
		lowerCAmelCase_      							=			xmod_layer.final_layer_norm.bias
		if bert_output.adapter_layer_norm is not None:
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.weight
			lowerCAmelCase_      							=			xmod_layer.adapter_layer_norm.bias

		if sorted(bert_output.adapter_modules.keys()	) != sorted(xmod_layer.adapter_modules.keys()	):
			raise AssertionError('''Lists of language adapters do not match.'''	)
		for lang_code, adapter in xmod_layer.adapter_modules.items():
			lowerCAmelCase_      							=			bert_output.adapter_modules[lang_code]
			lowerCAmelCase_      							=			xmod_layer.adapter_modules[lang_code]
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias
			lowerCAmelCase_      							=			from_adapter.fca.weight
			lowerCAmelCase_      							=			from_adapter.fca.bias

		# end of layer

	if xmod_sent_encoder.layer_norm is not None:
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.weight
		lowerCAmelCase_      							=			xmod_sent_encoder.layer_norm.bias

	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].dense.bias
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.weight
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''].out_proj.bias
	else:
		# LM Head
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.dense.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.layer_norm.bias
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.weight
		lowerCAmelCase_      							=			xmod.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	lowerCAmelCase_      							=			xmod.encode(_lowercase	).unsqueeze(0	)  # batch of size 1
	model.roberta.set_default_language(_lowercase	)

	lowerCAmelCase_      							=			model(_lowercase	)[0]
	if classification_head:
		lowerCAmelCase_      							=			xmod.model.classification_heads['''mnli'''](xmod.extract_features(_lowercase	)	)
	else:
		lowerCAmelCase_      							=			xmod.model(_lowercase			,   lang_id=[SAMPLE_LANGUAGE]	)[0]
	print(our_output.shape			,   their_output.shape	)
	lowerCAmelCase_      							=			torch.max(torch.abs(our_output - their_output	)	).item()
	print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
	lowerCAmelCase_      							=			torch.allclose(_lowercase			,   _lowercase			,   atol=1E-3	)
	print('''Do both models output the same tensors?'''			,   '''🔥''' if success else '''💩'''	)
	if not success:
		raise Exception('''Something went wRoNg'''	)

	Path(_lowercase	).mkdir(parents=_lowercase			,   exist_ok=_lowercase	)
	print(F"""Saving model to {pytorch_dump_folder_path}"""	)
	model.save_pretrained(_lowercase	)


if __name__ == "__main__":
		lowercase_							=							argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xmod_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		parser.add_argument(
		    '--classification_head', action='store_true', help='Whether to convert a final classification head.'
		)
		lowercase_							=							parser.parse_args()
		convert_xmod_checkpoint_to_pytorch(
		    args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)